AI 中文总结
针对高光谱图像数据量大难实时处理的问题，提出将密集运算迁移到特定框架结合XNNPACK后端进行优化的方法，在树莓派5平台部署，显著减少计算时间并保留融合质量，适用于相关遥感应用。
AI 中文摘要
遥感光学图像在众多应用中至关重要。高光谱图像虽能提取丰富信息，但因其数据量大，处理时计算负荷高，实时处理困难。此前提出的HSB-SV技术在树莓派上实现时计算时间长。本文将密集运算迁移到PyTorch及ONNX Runtime、ExecuTorch等边缘推理框架并结合XNNPACK后端进行计算优化，在树莓派5平台部署。实验结果表明计算时间显著减少，总执行时间从527.1毫秒降至356.7毫秒，降幅32.3%，且融合质量得以保留，更适用于嵌入式和边缘计算场景，特别是基于无人机的高光谱遥感应用。
英文摘要
Remote sensing optical images have become central to a wide range of applications. In particular, hyperspectral images, with their high spectral resolution, enable the extraction of rich information about the objects and materials present in the observed scene. Nevertheless, processing such data comes at the expense of a high computational load due to its large data volume, making real-time processing very difficult to achieve. Recently, we proposed an approach to investigate the feasibility of processing such data on a Raspberry Pi by implementing a hyperspectral super-resolution technique, namely HSB-SV. However, the implementation resulted in high computational time. To overcome this limitation, we apply computational optimization techniques based on migrating the most intensive operations to PyTorch and edge inference frameworks such as ONNX Runtime and ExecuTorch with the XNNPACK backend. The proposed optimized implementation is deployed on a Raspberry Pi 5 platform. Experimental results demonstrate a significant reduction in computational time, achieving a 1.48x overall speedup on the Raspberry Pi 5, the total execution time decreases significantly, from 527.1 ms to 356.7 ms corresponding to a 32.3% dminution, while fully preserving the fusion quality, making the approach more suitable for embedded and edge computing scenarios, particularly for UAV-based hyperspectral remote sensing applications.
Commentspaper is accepted for presentation at EDiS'2026: IEEE 5th International Conference on Embedded and Distributed Systems, Oran, Algeria, November 2-5, 2026
